Quantifying day-to-day evolution of choice patterns in public transit system with smart transit card data

Abstract

Existing studies proposed different models to capture day-to-day evolution of travelers’ choices and traffic dynamics. However, explicitly incorporating public transit service unreliability/reliability into day-to-day traffic modeling received very little attention. This study develops day-to-day models to explore how the unreliability of public transit service affects the day-to-day evolution of travel choices made by transit users in the Greater Sydney area. In particular, we consider two dynamical processes that incorporate transit service unreliability, i.e., travelers’ learning and perception updating process (LPUP) and proportional-switch adjustment pro- cess (PSAP). The conditions for the existence, uniqueness and stability of the fixed point of each model are analytically derived. These conditions are then examined using real-world public transit data from the Greater Sydney area. We find that with some aggregations and approximations, the system stability conditions at the fixed point are satisfied in both models. The observed weighted average flow change between two successive days is around 6.5% over the observation period, which may reflect the system stochasticity rather than instability. Among a series of empirical findings, it is noteworthy that in the Sydney case, the value of service schedule delay (late for schedule) is worth around 3.27 times of the in-vehicle time. Moreover, we find that the LPUP model more closely approximates real day-to-day travel choices than PSAP model, which reflects its stronger capability to capture the non-linear effects between cost perception and travel choices.